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deit-cifar100
wr123/deit-cifar100
deit-cifar100 is a machine learning model from wr123. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Dense fine-tuned weights of the 3 DeiT variants on CIFAR100 (starting from the ImageNet-1K official weights, low-LR fine-tuning, no distillation), produced by the LPViT pipeline strategy=dense. One subdirectory per mo…
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Updated Sep 7, 2026
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At a glance
- Access
- Public
- Created
- Sep 6, 2026
- Updated
- Sep 7, 2026
- SHA
- 8c77fa84
Model card
From the Hugging Face model README
deit-cifar100
Dense fine-tuned weights of the 3 DeiT variants on CIFAR100 (starting from the
ImageNet-1K official weights, low-LR fine-tuning, no distillation), produced by the
LPViT pipeline strategy=dense. One subdirectory per model:
| Model | Subdirectory | Reference accuracy |
|---|---|---|
| DeiT-Tiny | deit_tiny_patch16_224_cifar100/ | ~81.65% |
| DeiT-Small | deit_small_patch16_224_cifar100/ | ~87.54% |
| DeiT-Base | deit_base_patch16_224_cifar100/ | ~90.04% |
Each subdirectory contains model.safetensors (standard DeiT keys), config.json,
and a README.md (loading examples). Common input stats (CIFAR100 @224): mean=[0.5071, 0.4867, 0.4408], std=[0.2675, 0.2565, 0.2761].
Hyperparameters (dense fine-tune)
- Initialization: ImageNet-1K DeiT official weights (timm Hugging Face)
- Model / input: DeiT patch16 @224, 100 classes
- Epochs: 30
- Batch size: 128
- Optimizer: AdamW (eps=1e-8)
- Learning rate: effective 5e-5 (recipe lr=2e-4, scaled by lr * batch * world / 512)
- LR scheduler: cosine, min_lr=1e-5, warmup=5 epochs
- Weight decay: 5e-2
- Label smoothing: 0.1
- RandAugment: rand-m9-mstd0.5-inc1
- Mixup: 0.8
- CutMix: 1.0
- Random Erasing: 0.25
- Distillation: none (no teacher)
- Backbone: fully updated (not frozen)
- Precision: bf16
- Input normalization: mean=[0.5071, 0.4867, 0.4408], std=[0.2675, 0.2565, 0.2761]
- Created
- Sep 6, 2026
- Updated
- Sep 7, 2026